Equalização adaptativa e autodidata de canais lineares e não-lineares utilizando o algoritmo do módulo constante

Abstract

This work studies and proposes algorithms to perform blind equalization of linear and nonlinear channels inspired on the Constant Modulus Algorithm (CMA). The CMA works very well for modulations in which all points of the signal constellation have the same radius, like in Phase Shift Keying (PSK) modulations. However, when the constellation points are characterized by multiple radii, like in Quadrature Amplitude Modulation (QAM) signals, the CMA does not work properly in many situations. Thus, the techniques proposed here are designed to improve the performance of the CMA, in terms of speed of convergence and residual error, when working with signals transmitted with multiple magnitude, in particular with QAM signals. As well as for the CMA, these techniques should have a good compromise among performance, complexity and robustness. To do so, the techniques use the last decided symbol to estimate reference radius to the output of the equalizer. In fact, they can be seen as modifi cations of the CMA and of some of its derivatives for constellations with multiple radii. The proposition of stochastic gradient algorithms is concluded with the development of new adaptive blind techniques to equalize channels with a Wiener structure. A Wiener fi lter consists of a linear block with memory followed by a memoryless nonlinearity, by using the CMA. We develop expressions for the adaptation of the equalizer using a unified notation for three diff erent equalizer filter structures: i) a Hammerstein filter, ii) a diagonal Volterra filter and iii) a Volterra fi lter. A theoretical analysis of the main proposed technique, the Decision Directed Modulus Algorithm (DDMA), is also done. We study the convergence and the stability of the DDMA by means of an analysis of the minima of the DDM cost function. We also develop an analytic expression for the Excess Mean Square Error (EMSE) provided by the DDMA in the noiseless case. Then, we nd some interesting relationships among the DDM, the CM and the Wiener cost functions. We also develop a class of normalized algorithms and a class of Recursive Least Squares (RLS)-type algorithms for blind equalization inspired on the CMA-based techniques studied. Each family is composed of four algorithms with desirable properties and advantages over the original CM algorithms, specially when working with high-level QAM signals. Normalized and RLS techniques for equalization of Wiener channels are also developed. The behavior of the proposed classes of algorithms discussed is tested by computational simulations. We verify that the proposed techniques provide signifi cative gains in performance, in terms of speed of convergence and residual error, when compared to the classical algorithms.